PDF(Pubmed)
Abstract：
The evaluation of mammographic breast density, a critical indicator of breast cancer risk, is traditionally performed by radiologists via visual inspection of mammography images, utilizing the Breast Imaging-Reporting and Data System (BI-RADS) breast density categories. However, this method is subject to substantial interobserver variability, leading to inconsistencies and potential inaccuracies in density assessment and subsequent risk estimations. To address this, we present a deep learning-based automatic detection algorithm (DLAD) designed for the automated evaluation of breast density. Our multicentric, multi-reader study leverages a diverse dataset of 122 full-field digital mammography studies (488 images in CC and MLO projections) sourced from three institutions. We invited two experienced radiologists to conduct a retrospective analysis, establishing a ground truth for 72 mammography studies (BI-RADS class A: 18, BI-RADS class B: 43, BI-RADS class C: 7, BI-RADS class D: 4). The efficacy of the DLAD was then compared to the performance of five independent radiologists with varying levels of experience. The DLAD showed robust performance, achieving an accuracy of 0.819 (95% CI: 0.736-0.903), along with an F1 score of 0.798 (0.594-0.905), precision of 0.806 (0.596-0.896), recall of 0.830 (0.650-0.946), and a Cohen\'s Kappa (κ) of 0.708 (0.562-0.841). The algorithm achieved robust performance that matches and in four cases exceeds that of individual radiologists. The statistical analysis did not reveal a significant difference in accuracy between DLAD and the radiologists, underscoring the model\'s competitive diagnostic alignment with professional radiologist assessments. These results demonstrate that the deep learning-based automatic detection algorithm can enhance the accuracy and consistency of breast density assessments, offering a reliable tool for improving breast cancer screening outcomes.
摘要：
乳腺密度的评估，乳腺癌风险的关键指标，传统上由放射科医生通过乳房X线照相术图像的视觉检查来执行，利用乳腺成像报告和数据系统（BI-RADS）乳腺密度类别。然而,这种方法在观察者之间存在很大的可变性，导致密度评估和后续风险估计的不一致和潜在的不准确。为了解决这个问题，我们提出了一种基于深度学习的自动检测算法（DLAD），旨在自动评估乳腺密度。我们的多中心，多读者研究利用了来自三个机构的122个全视野数字乳房X线摄影研究的不同数据集（CC和MLO投影中的488张图像）。我们邀请了两位经验丰富的放射科医师进行回顾性分析,为72项乳房X线照相术研究（BI-RADSA类：18，BI-RADSB类：43，BI-RADSC类：7，BI-RADSD类：4）。然后将DLAD的功效与具有不同经验水平的五名独立放射科医师的表现进行比较。DLAD显示出强大的性能，达到0.819的准确度(95%CI：0.736-0.903)，F1得分为0.798（0.594-0.905），精度为0.806（0.596-0.896），召回0.830(0.650-0.946)，科恩的卡帕（κ）为0.708（0.562-0.841）。该算法实现了匹配的稳健性能，并且在四种情况下超过了单个放射科医生的稳健性能。统计分析并没有发现DLAD和放射科医师之间的准确性存在显着差异。强调该模型与专业放射科医生评估的竞争性诊断一致性。这些结果表明，基于深度学习的自动检测算法可以提高乳腺密度评估的准确性和一致性，为改善乳腺癌筛查结果提供了可靠的工具。
